Journal of Aerospace System Engineering (항공우주시스템공학회지)

The Society for Aerospace System Engineering (항공우주시스템공학회)
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Prediction of the Major Factors for the Analysis of the Erosion Effect on Atomic Oxygen in LEO Satellite Using a Machine Learning Method (LSTM)

  • Received : 2020.01.13
  • Accepted : 2020.04.01
  • Published : 2020.04.30
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Abstract

In this study, we investigated whether long short-term memory (LSTM) can be used in the future to predict F10.7 index data; the F10.7 index is a space environment factor affecting atomic oxygen erosion. Based on this, we compared the prediction performances of LSTM, the Autoregressive integrated moving average (ARIMA) model (which is a traditional statistical prediction model), and the similar pattern searching method used for long-term prediction. The LSTM model yielded superior results compared to the other techniques in the prediction period starting from the max/min points, but presented inferior results in the prediction period including the inflection points. It was found that efficient learning was not achieved, owing to the lack of currently available learning data in the prediction period including the maximum points. To overcome this, we proposed a method to increase the size of the learning samples using the sunspot data and to upgrade the LSTM model.

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References

  1. Y. G. Kim, E. S. Park, B. C. Kim, S. H Lee, and S. H. Lee, "The case study on the optimized forecasting methods of major factors for the analysis of the atomic oxygen erosion effect in LEO satellite," Proc. of KSAS 2019 Fall Conference, Cheju, Korea, pp. 203, November 2019.
  2. www.spenvis.oma.be.
  3. https://www.swpc.noaa.gov/news/solar-cycle-25-forecastupdate, Dec. 09, 2019.
  4. ftp://ftp.geolab.nrcan.gc.ca/data/solar_flux/daily_flux_values/.
  5. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. https://doi.org/10.1162/neco.1997.9.8.1735
  6. S.-K. Park, "Principle and formula calculation of LSTM," http://docs.likejazz.com/lstm/, May 30, 2018.
  7. M. Mishra, "Unboxing ARIMA models," https://towardsdatascience.com/unboxing-arima-models-1dc09d2746f8, Jun 11, 2018.
  8. Y. G. Kim, S. T. Lee, M. J. Baek, and S. H. Lee, "Prediction of atomic oxygen erosion for coating material of LEO satellite's solar array by using the real RAM direction accumulation method," Journal of Aerospace System Engineering, vol. 11, no. 5, pp. 1-5, 2017. https://doi.org/10.20910/JASE.2017.11.5.1